LUMI-lab: a Foundation Model-Driven Autonomous Platform Enabling Discovery of New Ionizable Lipid Designs for mRNA Delivery

Abstract The complexity of molecular discovery requires autonomous systems that efficiently explore vast and uncharted chemical spaces. While integrating artificial intelligence (AI) with robotic automation has accelerated discovery, its application remains constrained in fields with scarce historical data. One such challenge is the design of lipid nanoparticles (LNPs) for mRNA delivery, which has relied on expert-driven design and is hindered by limited datasets. Here, we introduce LUMI-lab, a self-driving lab (SDL) system that enables efficient learning with minimal wet-lab data by integrating a molecular foundation model with an automated active-learning experimental workflow. Through ten iterative cycles, LUMI-lab synthesized and evaluated over 1,700 LNPs, identifying ionizable lipids with superior mRNA transfection potency in human bronchial cells compared to clinically approved benchmarks. Unexpectedly, it autonomously uncovered brominated lipid tails as a novel feature enhancing mRNA delivery. In vivo validation further confirmed that inhalation of LNPs containing the top-performing lipid, LUMI-6, achieved 20.3% gene editing efficacy in lung epithelial cells in murine models, surpassing the highest efficiency reported for inhaled LNP-mediated CRISPR-Cas9 delivery in mice to our knowledge. These findings demonstrate LUMI-lab as a powerful, data-efficient platform for advancing mRNA delivery, highlighting the potential of AI-driven autonomous systems to accelerate innovation in material science and therapeutic discovery.